Any ball goal-reaching in simulation. Table REF  presents the mean and standard deviation of 5 seeds for each of the 7 algorithms considered after 120k trajectories (equivalent to \(\approx \)  6 days of continuous training on a physical robot), except for LFP [1]} which by definition only uses the initial demonstrations. Figure REF  shows the corresponding learning curves. On this task GoalsEye achieves 21% goal-reaching success within 30cm of the target and 11% within 20cm of the target after 120k total trajectories. 3.6k of these trajectories were the initial demonstrations, and the remainder were generated through self-supervised practice. GoalsEye improves \(>2\times \)  on the 30cm metric and \(\approx 3\times \)  on the 20cm metric compared to LFP which lacks a mechanism for continuous improvement. On this task GCSL [2]} fails to overcome the initial hard exploration problem as described in Section . One reason why the exploration problem is particularly hard in this setting is because in simulation the robot is initialized with the front edge of the paddle facing forward, reducing the probability of hitting the ball through random exploration.
